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Fantaxtic - Nested Bar Plots for Phyloseq Data

R 100.00%

ggplot2 microbiome phyloseq r r-package visualization barplot multilevel nested

fantaxtic's Introduction

fantaxtic

fantaxtic contains a set of functions to identify and visualize the most abundant taxa in phyloseq objects. It allows users to identify top taxa using any metric and any grouping, and plot the (relative) abundances of the top taxa using a nested bar plot visualisation. In the nested bar plot, colours or fills signify a top taxonomic rank (e.g. Phylum), and a gradient of shades and tints signifies levels at a nested taxonomic rank (e.g. Species). It is particularly useful to present an overview of microbiome sequencing, amplicon sequencing or metabarcoding data.

Note that fantaxtic is essentially a wrapper around ggnested, with some accessory functions to identify top taxa and to ensure that the plot is useful. Thus, the output is ggplot2 object, and can be manipulated as such.

Keywords: nested bar plot, phyloseq, taxonomy, most abundant taxa, multiple levels, shades, tints, gradient, 16S, ITS ,18S, microbiome, amplicon sequencing, metabarcoding

Installation

if(!"devtools" %in% installed.packages()){
  install.packages("devtools")
}
devtools::install_github("gmteunisse/fantaxtic")
devtools::install_github("gmteunisse/ggnested")

Citation

Please use the citation below. For BibTeX, us the 'Cite this repository' button.

Teunisse, G. M. (2022). Fantaxtic - Nested Bar Plots for Phyloseq Data (Version 2.0.1) [Computer software]. https://github.com/gmteunisse/Fantaxtic

Basic usage

The workflow consists of two parts:

Identify top taxa using either top_taxa or nested_top_taxa
Visualise the top taxa using nested_bar_plot

For basic usage, only a few lines of R code are required. To identify and plot the top 10 most abundant ASVs by their mean relative abundance, using Phylum as the top rank and Species as the nested rank, run:

require("fantaxtic")
require("phyloseq")
require("tidyverse")
require("magrittr")
require("ggnested")
require("knitr")
require("gridExtra")

data(GlobalPatterns)
top_asv <- top_taxa(GlobalPatterns, n_taxa = 10)
plot_nested_bar(ps_obj = top_asv$ps_obj,
                top_level = "Phylum",
                nested_level = "Species")

To identify and plot the top 3 most abundant Phyla, and the top 3 most abundant species within those Phyla, run:

top_nested <- nested_top_taxa(GlobalPatterns,
                              top_tax_level = "Phylum",
                              nested_tax_level = "Species",
                              n_top_taxa = 3, 
                              n_nested_taxa = 3)
plot_nested_bar(ps_obj = top_nested$ps_obj,
                top_level = "Phylum",
                nested_level = "Species")

`top_taxa`

This function identifies the top n taxa by some metric (e.g. mean, median, variance, etc.) in a phyloseq object. It outputs a table with the top taxa, as well as a phyloseq object in which all other taxa have been merged into a single taxon.

Taxonomic rank

By default, top_taxa runs the analysis at the ASV level; however, if a tax_level is specified (e.g. Species), it first agglomerates the taxa in the phyloseq object at that rank and then runs the analysis. Note that taxonomic agglomeration makes the assumption that taxa with the same name at all ranks are identical. This also includes taxa with missing annotations (NA). By default, top_taxa does not considered taxa with an NA annotation at tax_level, but this can be overcome by setting include_na_taxa = T.

top_species <- top_taxa(GlobalPatterns,
                        n_taxa = 10, 
                        tax_level = "Species")
top_species$top_taxa %>%
  mutate(abundance = round(abundance, 3)) %>%
  kable(format = "markdown")

tax_rank	taxid	abundance	Kingdom	Phylum	Class	Order	Family	Genus	Species
4	326977	0.010	Bacteria	Actinobacteria	Actinobacteria	Bifidobacteriales	Bifidobacteriaceae	Bifidobacterium	Bifidobacteriumadolescentis
9	9514	0.005	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Actinobacillus	Actinobacillusporcinus
1	94166	0.014	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
8	469778	0.005	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Bacteroidescoprophilus
6	471122	0.006	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotella	Prevotellamelaninogenica
10	248140	0.005	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Bacteroidescaccae
7	470973	0.005	Bacteria	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Ruminococcus	Ruminococcustorques
3	171551	0.011	Bacteria	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Faecalibacterium	Faecalibacteriumprausnitzii
2	98605	0.013	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis
5	114821	0.009	Bacteria	Firmicutes	Clostridia	Clostridiales	Veillonellaceae	Veillonella	Veillonellaparvula

Grouping

Furthermore, if one or more grouping factors are specified in grouping, it will calculate the top n taxa using the samples in each group, rather than using all samples in the phyloseq object. This makes it possible to for example identify the top taxa in each sample, or the top taxa in each treatment group.

top_grouped <- top_taxa(GlobalPatterns,
                        n_taxa = 1,
                        grouping = "SampleType")
top_grouped$top_taxa %>%
  mutate(abundance = round(abundance, 3)) %>%
  kable(format = "markdown")

SampleType	tax_rank	taxid	abundance	Kingdom	Phylum	Class	Order	Family	Genus	Species
Freshwater (creek)	1	549656	0.464	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA	NA	NA
Freshwater	1	279599	0.216	Bacteria	Cyanobacteria	Nostocophycideae	Nostocales	Nostocaceae	Dolichospermum	NA
Ocean	1	557211	0.071	Bacteria	Cyanobacteria	Synechococcophycideae	Synechococcales	Synechococcaceae	Prochlorococcus	NA
Tongue	1	360229	0.145	Bacteria	Proteobacteria	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA
Mock	1	550960	0.117	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacteriales	Enterobacteriaceae	Providencia	NA
Sediment (estuary)	1	319044	0.080	Bacteria	Proteobacteria	Deltaproteobacteria	Desulfobacterales	Desulfobulbaceae	NA	NA
Feces	1	331820	0.137	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	NA
Soil	1	36155	0.013	Bacteria	Acidobacteria	Solibacteres	Solibacterales	Solibacteraceae	CandidatusSolibacter	NA
Skin	1	98605	0.103	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis

Ranking metric

Lastly, any metric can be used to rank taxa by specifying a function through FUN. The mean is used by default, but depending on your analysis, you might want to use the median, variance, maximum or any other function that takes as input a numeric vector and outputs a single number.

top_max <- top_taxa(GlobalPatterns,
                        n_taxa = 10,
                        FUN = max)
top_max$top_taxa %>%
  mutate(abundance = round(abundance, 3)) %>%
  kable(format = "markdown")

tax_rank	taxid	abundance	Kingdom	Phylum	Class	Order	Family	Genus	Species
4	329744	0.266	Bacteria	Actinobacteria	Actinobacteria	Actinomycetales	ACK-M1	NA	NA
1	549656	0.500	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA	NA	NA
2	279599	0.432	Bacteria	Cyanobacteria	Nostocophycideae	Nostocales	Nostocaceae	Dolichospermum	NA
3	360229	0.270	Bacteria	Proteobacteria	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA
8	94166	0.198	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
9	484436	0.196	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Moraxellaceae	NA	NA
5	331820	0.230	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	NA
7	189047	0.207	Bacteria	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	NA	NA
6	98605	0.223	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis
10	114821	0.187	Bacteria	Firmicutes	Clostridia	Clostridiales	Veillonellaceae	Veillonella	Veillonellaparvula

`nested_top_taxa`

This function identifies the top n taxa at a taxonomic rank (e.g. Phylum) and the top m nested taxa at a lower taxonomic rank (e.g. Species) by some metric (e.g. mean, median, variance, etc.) in a phyloseq object. Internally, it makes use of top_taxa, and therefore uses many of the same options. Like top_taxa, it agglomerates taxa at the specified taxonomic ranks before identifying the top taxa. It outputs a table with the top taxa, as well as a phyloseq object in which all non-top taxa have been merged, both at the top_tax_level and at the nested_tax_level. This function is especially nice for providing overviews of your data, as it shows the relative abundance of each select top_tax_level taxon.

top_nested <- nested_top_taxa(GlobalPatterns,
                              top_tax_level = "Phylum",
                              nested_tax_level = "Species",
                              n_top_taxa = 3, 
                              n_nested_taxa = 3, 
                              nested_merged_label = "NA and other <tax>")
top_nested$top_taxa %>%
  mutate(top_abundance = round(top_abundance, 3),
         nested_abundance = round(nested_abundance, 3)) %>%
  kable(format = "markdown")

taxid	top_abundance	nested_abundance	top_tax_rank	nested_tax_rank	Kingdom	Phylum	Class	Order	Family	Genus	Species
200741	0.295	0.072	1	3	Bacteria	Proteobacteria	Betaproteobacteria	Burkholderiales	NA	Methylibium	Methylibiumpetroleiphilum
94166	0.295	0.122	1	1	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
236788	0.295	0.094	1	2	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacteriales	Enterobacteriaceae	Edwardsiella	Edwardsiellaictaluri
322235	0.173	0.110	3	2	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Bacteroidesuniformis
471122	0.173	0.147	3	1	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotella	Prevotellamelaninogenica
248140	0.173	0.076	3	3	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Bacteroidescaccae
171551	0.189	0.162	2	1	Bacteria	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Faecalibacterium	Faecalibacteriumprausnitzii
98605	0.189	0.116	2	2	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis
114821	0.189	0.089	2	3	Bacteria	Firmicutes	Clostridia	Clostridiales	Veillonellaceae	Veillonella	Veillonellaparvula

`plot_nested_bar`

This function is analogous in use to the phyloseq function plot_bar, but plots the abundances of taxa in each sample at two levels: a top level (e.g. Phylum) using colours, and a nested level (e.g. Species) using shades and tints of each colour. It is intended to be used in conjunction with top_taxa or nested_top_taxa, but works with any phyloseq object. The output is a ggplot2 object that is generated by ggnested, which means it can be further customized by the user, for example with faceting, themes, labels et cetera. Also see the documentation for ggnested.

plot_nested_bar(top_nested$ps_obj,
                top_level = "Phylum",
                nested_level = "Species",
                nested_merged_label = "NA and other <tax>",
                legend_title = "Phylum and species") +
  facet_wrap(~SampleType,
             scales = "free_x") +
  labs(title = "Relative abundances of the top 3 species for each of the top 3 phyla") +
  theme(plot.title = element_text(hjust = 0.5, 
                                  size = 8, 
                                  face = "bold"),
        legend.key.size = unit(10, 
                               "points"))

Palette

plot_nested_bar automatically generates as many colours as there are top_level taxa, starting from a base_clr. Furthermore, it colours the merged non-top taxa a different merged_clr, by default grey. Sometimes it is necessary to alter the colours of the plot, which is why custom top_level palettes can be provided. If the palette is named, the colours will be assigned appropriately. Note that it is not necessary to provide a complete palette; any missing colours will get generated automatically. This can be useful if you are trying to match colours between different plots.

plot_nested_bar(top_nested$ps_obj,
                top_level = "Phylum",
                nested_level = "Species",
                nested_merged_label = "NA and other",
                palette = c(Bacteroidetes = "red", 
                            Proteobacteria = "blue"),
                merged_clr = "black")

Sample order

By default, plot_nested_bar orders samples alphabetically. However, sometimes it may be insightful to alter the sample ordering, for example based on grouping or the abundance of a specific taxon. This can be achieved by supplying a character vector with the sample names in the desired order to sample_order.

# Order samples by the total abundance of Proteobacteria
sample_order <- psmelt(top_nested$ps_obj) %>%
  data.frame() %>%
  
  # Calculate relative abundances
  group_by(Sample) %>%
  mutate(Abundance = Abundance / sum(Abundance)) %>%
  
  # Sort by taxon of interest
  filter(Phylum == "Proteobacteria") %>%
  group_by(Sample) %>%
  summarise(Abundance = sum(Abundance)) %>%
  arrange(Abundance) %>% 
  
  # Extract the sample order
  pull(Sample) %>%
  as.character()

# Plot
plot_nested_bar(top_nested$ps_obj,
                "Phylum",
                "Species",
                sample_order = sample_order,
                nested_merged_label = "NA and other")

Advanced usage

The plot_nested_bar function will suffice for most purposes. However, sometimes, additional control over the plot is required. While some aspects can be controlled by altering additional arguments of plot_nested_bar, it may sometimes be necessary to generate the plot from scratch. plot_nested_bar is nothing more than a wrapper around the following functions, and can therefore be recreated manually if required:

Generate a palette using taxon_colours
Generate names for NA taxa using name_na_taxa
Label identical taxa using label_duplicate_taxa
Convert the phyloseq object to a data frame using psmelt
Relevel the merged taxa using move_label and move_nested_labels
Reorder the taxa
Generate a nested barplot using ggnested

Thus, for advanced usage, copy the code chunk below and modify it to your requirements.

# Get the top taxa
top_level <- "Phylum"
nested_level <- "Species"
sample_order <- NULL
top_asv <- top_taxa(GlobalPatterns, n_taxa = 10)

# Generate a palette based  on the phyloseq object
pal <- taxon_colours(top_asv$ps_obj,
                     tax_level = top_level)

# Create names for NA taxa
ps_tmp <- top_asv$ps_obj %>%
  name_na_taxa()

# Add labels to taxa with the same names
ps_tmp <- ps_tmp %>%
  label_duplicate_taxa(tax_level = nested_level)

# Convert physeq to df
psdf <- psmelt(ps_tmp)

# Move the merged labels to the appropriate positions in the plot:
# Top merged labels need to be at the top of the plot,
# nested merged labels at the bottom of each group
psdf <- move_label(psdf = psdf,
                   col_name = top_level,
                   label = "Other",
                   pos = 0)
psdf <- move_nested_labels(psdf,
                           top_level = top_level,
                           nested_level = nested_level,
                           top_merged_label = "Other",
                           nested_label = "Other",
                           pos = Inf)

# Reorder samples
if(!is.null(sample_order)){
  if(all(sample_order %in% unique(psdf$Sample))){
    psdf <- psdf %>%
      mutate(Sample = factor(Sample, levels = sample_order))
  } else {
    stop("Error: not all(sample_order %in% sample_names(ps_obj)).")
  }

}

# Generate a base plot
p <- ggnested(psdf,
              aes_string(main_group = top_level,
                         sub_group = nested_level,
                         x = "Sample",
                         y = "Abundance"),
              main_palette = pal) +
  scale_y_continuous(expand = c(0, 0)) +
  theme_nested(theme_light) +
  theme(axis.text.x = element_text(hjust = 1, vjust = 0.5, angle = 90))

# Add relative abundances
p <- p + geom_col(position = position_fill())
p

`taxon_colours`

This function generates a colour for each taxon at a specified rank in a phyloseq object. Custom palettes can also be provided, and if they are named, colours wil be assigned appropriately.

# Function to plot the colours in a palette
plot_colours <- function(pal){
  pal %>%
    data.frame(name = names(.),
               colour = .) %>%
    ggplot(aes(x = 1,
               y = name,
               fill = colour,
               label = paste(name, "-", colour))) +
    geom_tile() +
    geom_text() +
    scale_fill_identity() +
    scale_x_discrete(expand = c(0,0)) +
    scale_y_discrete(expand = c(0,0)) +
    theme(axis.text = element_blank(),
          axis.title = element_blank(),
          axis.ticks = element_blank(),
          plot.title = element_text(hjust = 0.5,
                                    size = 12))
}

# Get top taxa and generate a palette
pal <- taxon_colours(top_asv$ps_obj, tax_level = "Phylum")
p1 <- plot_colours(pal) +
  ggtitle("Default")

# Generate a palette with a different base_clr
pal2 <- taxon_colours(top_asv$ps_obj, tax_level = "Phylum", base_clr = "blue")
p2 <- plot_colours(pal2) +
  ggtitle("Base colour blue")

# Provide a custom incomplete palette
pal3 <- taxon_colours(top_asv$ps_obj, 
                     tax_level = "Phylum", 
                     palette = c(Cyanobacteria = "blue", 
                                 Bacteroidetes = "pink"))
p3 <- plot_colours(pal3) +
  ggtitle("Incomplete custom palette")
grid.arrange(p1, p2, p3, nrow = 1)

`name_na_taxa`

More often than not, ASVs will not have a complete taxonomic annotation down to the species level. In phyloseq object, this results in NA for any unavailable taxonomic rank. This creates issues when trying to plot at a low taxonomic rank such as Species. name_na_taxa resolves this problem by assigning the name of the lowest known taxonomic rank for every NA value in each ASV. To make it clear that the newly inferred name is not specific to the rank, it includes the rank from which the name was inferred in the new name. This can be turned off by setting include_rank = F.

# Fill in names for NA taxa, including their rank
ps_tmp <- name_na_taxa(top_asv$ps_obj)
tax_table(ps_tmp) %>%
  kable(format = "markdown")

	Kingdom	Phylum	Class	Order	Family	Genus	Species
549322	Other	Other	Other	Other	Other	Other	Other
329744	Bacteria	Actinobacteria	Actinobacteria	Actinomycetales	ACK-M1	Unknown ACK-M1 (Family)	Unknown ACK-M1 (Family)
317182	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	Unknown Stramenopiles (Order)	Unknown Stramenopiles (Order)	Unknown Stramenopiles (Order)
549656	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	Unknown Stramenopiles (Order)	Unknown Stramenopiles (Order)	Unknown Stramenopiles (Order)
279599	Bacteria	Cyanobacteria	Nostocophycideae	Nostocales	Nostocaceae	Dolichospermum	Unknown Dolichospermum (Genus)
360229	Bacteria	Proteobacteria	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	Unknown Neisseria (Genus)
94166	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
550960	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacteriales	Enterobacteriaceae	Providencia	Unknown Providencia (Genus)
158660	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Unknown Bacteroides (Genus)
331820	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Unknown Bacteroides (Genus)
98605	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis

# Leave the rank out and alter the label
ps_tmp <- name_na_taxa(top_asv$ps_obj, 
                       include_rank = F, 
                       na_label = "NA <tax>")
tax_table(ps_tmp) %>%
  kable(format = "markdown")

	Kingdom	Phylum	Class	Order	Family	Genus	Species
549322	Other	Other	Other	Other	Other	Other	Other
329744	Bacteria	Actinobacteria	Actinobacteria	Actinomycetales	ACK-M1	NA ACK-M1	NA ACK-M1
317182	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA Stramenopiles	NA Stramenopiles	NA Stramenopiles
549656	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA Stramenopiles	NA Stramenopiles	NA Stramenopiles
279599	Bacteria	Cyanobacteria	Nostocophycideae	Nostocales	Nostocaceae	Dolichospermum	NA Dolichospermum
360229	Bacteria	Proteobacteria	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA Neisseria
94166	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
550960	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacteriales	Enterobacteriaceae	Providencia	NA Providencia
158660	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	NA Bacteroides
331820	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	NA Bacteroides
98605	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis

`label_duplicate_taxa`

Another issue is that a single taxon may be represented by multiple ASVs, especially when low-rank annotations such as Species are missing. For some studies, it may be important to differentiate between these ASVs. Therefore, ASVs with the same taxonomy need to be assigned unique label to differentiate between them. label_duplicate_taxa identifies identical taxa and assigns either a count or the ASV name (taken from row.names(tax_table(ps_obj))) to these taxa.

# Label the lowest non-NA level
ps_tmp <- label_duplicate_taxa(top_asv$ps_obj, 
                               tax_level = "Genus")
tax_table(ps_tmp)  %>%
  kable(format = "markdown")

	Kingdom	Phylum	Class	Order	Family	Genus	Species
549322	Other	Other	Other	Other	Other	Other	Other
329744	Bacteria	Actinobacteria	Actinobacteria	Actinomycetales	ACK-M1	NA	NA
317182	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA	NA	NA
549656	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA	NA	NA
279599	Bacteria	Cyanobacteria	Nostocophycideae	Nostocales	Nostocaceae	Dolichospermum	NA
360229	Bacteria	Proteobacteria	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA
94166	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
550960	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacteriales	Enterobacteriaceae	Providencia	NA
158660	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides 1	NA
331820	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides 2	NA
98605	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis

# Use ASVs as ids rather than counts
ps_tmp <- label_duplicate_taxa(top_asv$ps_obj,
                               tax_level = "Genus",
                               asv_as_id = T, 
                               duplicate_label = "<tax> ASV <id>")
tax_table(ps_tmp) %>%
  kable(format = "markdown")

	Kingdom	Phylum	Class	Order	Family	Genus	Species
549322	Other	Other	Other	Other	Other	Other	Other
329744	Bacteria	Actinobacteria	Actinobacteria	Actinomycetales	ACK-M1	NA	NA
317182	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA	NA	NA
549656	Bacteria	Cyanobacteria	Chloroplast	Stramenopiles	NA	NA	NA
279599	Bacteria	Cyanobacteria	Nostocophycideae	Nostocales	Nostocaceae	Dolichospermum	NA
360229	Bacteria	Proteobacteria	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA
94166	Bacteria	Proteobacteria	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	Haemophilusparainfluenzae
550960	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacteriales	Enterobacteriaceae	Providencia	NA
158660	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides ASV 158660	NA
331820	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides ASV 331820	NA
98605	Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcussanguinis

`move_label` & `move_nested_labels`

move_label and move_nested_label reorder the factors in a specified column of a dataframe, so that in plot_nested_bar they appear in the right position. This is mainly used to place the Other merged taxa at the top of the plot, and the Other <tax> nested merged taxa at the bottom of each group. However, it can be used to move any taxon to any position. For example, if you are ordering your plot by the abundance of a specific Phylum, it may be desirable to make that Phylum appear at the bottom of the plot using pos = Inf.

# Turn physeq object into a dataframe
ps_tmp <- name_na_taxa(top_asv$ps_obj)
ps_tmp <- label_duplicate_taxa(ps_tmp, tax_level = "Species")
psdf <- psmelt(ps_tmp)
levels(as.factor(psdf$Phylum))
#> [1] "Actinobacteria" "Bacteroidetes"  "Cyanobacteria"  "Firmicutes"    
#> [5] "Other"          "Proteobacteria"

# Move the other label to the start, and Bacteroidetes to the end
psdf <- move_label(psdf, col_name = "Phylum", label =  "Other", pos = 0)
psdf <- move_label(psdf, col_name = "Phylum", label =  "Bacteroidetes", pos = Inf)
levels(psdf$Phylum)
#> [1] "Other"          "Actinobacteria" "Cyanobacteria"  "Firmicutes"    
#> [5] "Proteobacteria" "Bacteroidetes"

Likewise, any nested label can be moved to any desired position. grep will be used to find any taxon that contains the nested_label, after which it will be moved to the desired nested position. Here we flip the order of the two Unknown Bacteroides.

levels(as.factor(psdf$Species))
#>  [1] "Haemophilusparainfluenzae"       "Other"                          
#>  [3] "Streptococcussanguinis"          "Unknown ACK-M1 (Family)"        
#>  [5] "Unknown Bacteroides (Genus) 1"   "Unknown Bacteroides (Genus) 2"  
#>  [7] "Unknown Dolichospermum (Genus)"  "Unknown Neisseria (Genus)"      
#>  [9] "Unknown Providencia (Genus)"     "Unknown Stramenopiles (Order) 1"
#> [11] "Unknown Stramenopiles (Order) 2"
psdf <- move_nested_labels(psdf, 
                           top_level = "Phylum", 
                           nested_level = "Species",
                           top_merged_label = "Other",
                           nested_label = "Unknown Bacteroides 1",
                           pos = Inf)
levels(psdf$Species)
#>  [1] "Unknown ACK-M1 (Family)"         "Unknown Bacteroides (Genus) 1"  
#>  [3] "Unknown Bacteroides (Genus) 2"   "Unknown Dolichospermum (Genus)" 
#>  [5] "Unknown Stramenopiles (Order) 1" "Unknown Stramenopiles (Order) 2"
#>  [7] "Streptococcussanguinis"          "Haemophilusparainfluenzae"      
#>  [9] "Unknown Neisseria (Genus)"       "Unknown Providencia (Genus)"    
#> [11] "Other"

`ggnested`

Finally, the melted phyloseq object can be plotted using ggnested and ggplot. This gives full control over all aspects of the plot, including details such as the bar width. For more details, see the documentation of ggnested.

# Generate a base plot
p <- ggnested(psdf,
              aes_string(main_group = top_level,
                         sub_group = nested_level,
                         x = "Sample",
                         y = "Abundance"),
              main_palette = pal) +
  scale_y_continuous(expand = c(0, 0)) +
  theme_nested(theme_light) +
  theme(axis.text.x = element_text(hjust = 1, vjust = 0.5, angle = 90))

# Add relative abundances
p <- p + geom_col(position = position_fill(), width = 0.5)
p

Alternative plot types

By using ggnested directly rather than using plot_nested_bar, any type of plot can be created that is available through ggplot2. For example, you could create a boxplot that is grouped and coloured by phylum, and shaded by species:

# Create a boxplot instead of a barplot
psdf_rel <- psdf %>%
  group_by(Sample) %>%
  mutate(Abundance = Abundance / sum(Abundance)) %>%
  ungroup() %>%
  filter(Phylum != "Other")
p <- ggnested(psdf_rel,
              aes_string(main_group = top_level,
                         sub_group = nested_level,
                         x = "Phylum",
                         y = "Abundance",
                         grouping = "Species"),
              main_palette = pal,
              main_keys = T) +
  scale_y_continuous(expand = c(0, 0)) +
  theme_nested(theme_light) +
  theme(axis.text.x = element_text(hjust = 1, vjust = 0.5, angle = 90))
p + geom_boxplot(alpha = 0.5)

What happened to `fantaxtic_bar`?

All the functions in fantaxtic that were required to generate a fantaxtic_bar, such as get_top_taxa, have been deprecated. However, they are still functional and can be called. Many warnings will be issued, but this does not affect their functionality. See help("fantaxtic-deprecated") for deprecated functions.

ps_tmp <- get_top_taxa(GlobalPatterns, n = 10)
ps_tmp <- name_taxa(ps_tmp, label = "Unkown", species = T, other_label = "Other")
fantaxtic_bar(ps_tmp, color_by = "Phylum", label_by = "Species", other_label = "Other")

fantaxtic's People

Contributors

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tarah28 microsud marvillar

fantaxtic's Issues

Repeated sampling, paired bars

Hi,
It might be that this is the wrong forum for this question, and if so, I apologize.

I have a dataset with 800 samples from 62 participants. All participants were sampled 2 times, and 11 have been sampled three times with 6 different sample types.

I'd like to generate a bar plot that compares the relative abundances for these individuals (by sample type) at two (three) different time points (ie 6 charts, rel abundance at the y-axis, at the x-axis individual samples grouped by participant (2-3 bars per participant).

Would that be possible using your package? Grateful for any help!

Rune Nielsen, Bergen, Norway

Bold legend titles fails when changing theme

The setting theme_classic(), the headers within the legends (Actinobacteria, Firmicutes, ...) are not in bold anymore, but rather gets ** ** around the name. Suggestions on how to fix this?


plot_nested_bar(ps_obj = top_nested$ps_obj,
                relative_abundances = T,
                #sample_order = sample_order,
                palette = c(Bacteroidetes = "#de2d26", 
                            Firmicutes = "#a1d99b",
                            Fusobacteria ="#08306b"),
                merged_clr = "grey90",
                top_level = "Phylum",
                nested_level = "Genus") + 
  facet_wrap(~Group, scales="free_x",ncol=1) + 
  theme_classic(base_size = 6) + 
  theme(legend.key.size = unit(6,"points")) +
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  guides(fill=guide_legend(ncol =1))

Nested_top_taxa

Hi:

Thank you for developing this excellent package!

I want to map the Top 10 phyla, with 3 Order levels nested within each Phylum, but I am confused about the results. I don't see any Order shown in p__Firmicutes; however, taxonomy annotation results indicate that all p__Firmicutes are annotated to the Order level.

`top_nested <- nested_top_taxa(ps,
top_tax_level = "Phylum",
nested_tax_level = "Order",
n_top_taxa = 9,
n_nested_taxa = 3)

plot_nested_bar(ps_obj = top_nested$ps_obj,
top_level = "Phylum",
nested_level = "Order") +
facet_nested_wrap(~ SoilType + Deposition, scales = "free_x", nrow = 3)`

I uploaded my data at the same time：

ps.zip

n_taxa = is not working for me

The n_taxa option does not work for me. Please see below. Also the tax_level = "" option doesnt work. It brings this error
unused arguments (n_taxa = 10, tax_level = "Species")

top_asv <- top_taxa(GlobalPatterns, n_taxa = 10)
Error in top_taxa(GlobalPatterns, n_taxa = 10) : unused argument (n_taxa = 10)
top_asv <- top_taxa(GlobalPatterns)

Most abundant genera across a complete phyloseq object

Hi,

I'm trying to use fantaxtic to get the top 10 most abundant genera across a complete phyloseq object and list the relative abundances of these top genera. I was trying to do this like so:

top <- top_taxa(physeq, 
                tax_level = "Genus", 
                n_taxa = 10)

But I get:

Error in dimnames(x) <- dn : 
  length of 'dimnames' [1] not equal to array extent

Maybe this has something to do with the fact that I classified my OTUs using Lotus2, and therefore there are "?" in my tax table?

2-level relative abundance bar plot of microbial metagenomics data: help with code

Hi,

I have been trying to visual my microbial metagenomics data (i.e., phyloseq objects) in a relative abundance bar plot using Phylum as top rank and Class as nested rank, as well as doing the same with a relative abundance bar plot using Functional information as top rank and Class as nested rank, for each sampling depth (samples). The fantaxtic R package seems to be perfect for that, but when I plot the data after applying the function nested_top_taxa() I get this error message:

Error in h(simpleError(msg, call)) :
error in evaluating the argument 'object' in selecting a method for function 'tax_table': subscript out of bounds

I also get the following error message when using the function plot_nested_bar() :

Error in nested_palette(data, group, subgroup, gradient_type, min_l, max_l, :
Error: 6 values required in clr.pal, 5 provided.

This keeps happening regardless of the number of n_top_taxa or n_nested_taxa I choose in the nested_top_tax() function.

Could I ask for any recommendations/ insights you may have please?

This is my code and data at the moment:

ps_obj1 #my phyloseq object

phyloseq-class experiment-level object
otu_table() OTU Table: [ 2163 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 8 sample variables ]
tax_table() Taxonomy Table: [ 2163 taxa by 6 taxonomic ranks ] #taxonomic ranks = "t"= trophy; "k" = kingdom; "p"= phylum; "c" = Class; "o"= order; "f"= family

tax_table(ps_obj1) #visualise tax_table()

Taxonomy Table: [2163 taxa by 6 taxonomic ranks]:
t k p c
OTU_1 "phagotrophs" "Eukaryota" "Stramenopiles_X" "Bicoecea"
o f
OTU_1 "Anoecales" "Caecitellaceae"

nov_top_nested1 <- nested_top_taxa(ps_obj1, #Phylum as top rank, Class as nested rank
top_tax_level = "p" ,
nested_tax_level = "c",
n_top_taxa= 7,
n_nested_taxa = 3)

tax_nov_barplot1 <- plot_nested_bar(ps_obj = nov_top_nested1$ps_obj,
top_level = "p",
nested_level = "c")

Error in nested_palette(data, group, subgroup, gradient_type, min_l, max_l, :
Error: 7 values required in clr.pal, 6 provided.

nov_top_nested2 <- nested_top_taxa(ps_obj1, #Trophy as top rank, Class as nested rank
top_tax_level = "t" ,
nested_tax_level = "c",
n_top_taxa = 5,
n_nested_tax_level= 3)

Error in h(simpleError(msg, call)) :
error in evaluating the argument 'object' in selecting a method for function 'tax_table': subscript out of bounds

troph_oct_barplot1 <- plot_nested_bar(ps_obj = nov_top_nested2$ps_obj,
top_level = "t",
nested_level = "c")

Thank you in advance for your help.

Best wishes,
Naomi

Relative abundance with others

This code needs a data_matrix to work, is there another way to work it without, with only otu_table and taxa_table?

data(GlobalPatterns)
top_asv <- top_taxa(GlobalPatterns, n_taxa = 10)
plot_nested_bar(ps_obj = top_asv$ps_obj,
top_level = "Phylum",
nested_level = "Species")

Color palette, rgb, color name etc... or possible insertion from other packages?

Hi!

I'm starting to use fantaxtic, I appreciate the integrated version for the relative abundances and so on.

I was wondering if there is a way to implement a personalised palette (e.g. from RColorBrewer) to be used inside the plot_nested_bar( palette= ...).

Do you have an already implemented solution or can you give me a suggestion to do so?

Extract data that went into the bar plot

Hi, I need a table showing the relative abundance of each taxon in each sample. Is there a way to extract that data from the fantaxtic bar plot?

I also want to double check.... "Other" just means taxa that weren't in the top most abundant, right? And not that it's just a taxon that isn't identified?

Thank you!

How can I group samples by fixed effects on the x axis instead of the model?

#ecto samples

In the below code: I'm using a phyloseq object ecto. I want to have the x-axis separated by a fixed effect, "soil type" between soil 1 and soil 2. So instead of individual samples on the x axis, which is shown in all your examples, I want the taxa grouped into a single column for either soil 1 or soil 2.

is there anyway to do that? I'm a total noob to this type of data so I'm at a loss.

top_nestedspod <- nested_top_taxa(ecto,
top_tax_level = "phylum",
nested_tax_level = "genus",
n_top_taxa = 6,
n_nested_taxa = 6)

plot_nested_bar(ps_obj = top_nestedspod$ps_obj,
top_level = "phylum",
nested_level = "genus",
nested_merged_label = "NA and other",
legend_title = "Phylum and Genus")+
facet_wrap(~Carryover, scales = "free_x") +
theme(plot.title = element_text(hjust = 0.5,
size = 8,
face = "bold"),
legend.key.size = unit(10,"points"))

I'm getting a nice output, but I really want the mean grouping of each fixed effect on the x axis (soil type). I found your "grouping" section in the READme file, but I'm not seeing how to incorporate anything into the above code.

Working with a taxonomic subset?

Hi, thank you for the package, it's very easy and useful for manipulating the top ranked groups in a dataset!

Is it possible to work with a taxonomic subset, coupled with the core function of picking out the most abundant features, from within fantaxtic while retaining all the other (relative) abundance information of groups that were not subsetted?

E.g. from the GlobalPatterns dataset, the 10 most abundant ASVs are phyla that include Cyanobacteria. Now, only from Cyanobacteria pick the top 20 most abundant ASVs, plot a nested bar with top_level = "Class", nested_level = "Species" so that all other (abundant and non abundant) features that were not subsetted are placed in other?

I know how to subset specific taxonomic groups from a phyloseq object, but the total rel. abundance information is clearly not retained then. Even if do export the relative abundance instead of raw counts, or manually change the abundance values in a top_taxa dataframe, not sure if fantaxtic would accept those if the assumption is that the sum should always be 1?

Thanks!

Customize x-axis

Hey! I'm already loving fantaxtic so much!

I was just wondering if there have been an update (or maybe a function that I'm ignoring) where I can customize de x-axis and instead of 'Sample' choose another column in metadata?

Thanks

nested_top_taxa

Hello, sorry to bother you.
Maybe you can help me. I have a problem with the nested_top_taxa command. I always get this error message:
Error in apply(top_top[, ranks], 1, paste, collapse = "_") :
dim(X) must have a positive length.
Everything works with the example file, but not with mine. I compared the phyloseq files and everything is as it should be. Top_taxa works, but as soon as I want to do nested_top_taxa, the error occurs.
Kind regards

grouped samples in the X-axis

Hi!!

Fantaxtic package is .... fantastic.
I was wondering if it was possible to display the bars according to the sample group in the x-axis (e.g. sick, healthy) instead of the sample names.
Thanks

Change X axis order with fantaxtic_bar

Hi, I am trying to change the X axis order on my fantaxtic_bar plot - I'm sad to see that was deprecated, by the way! It is my favorite package for microbiome data.

`ps_norm <- transform_sample_counts(ps, function(x) x / sum(x) )
family <- tax_glom(ps, taxrank = "Family")

top15 <- get_top_taxa(family, 15, relative = TRUE, discard_other = FALSE, other_label = "Other")
top15 <- name_taxa(top15, label = "", species = F, other_label = "Other")

sample_data(top15)$Month = factor(sample_data(top15)$Month, levels = c("20-Nov","21-Apr","21-May","21-Jul", "22-Feb", "22-Mar", "22-Apr"))
sample_names(top15) <- sample_data(top15)$Month
fantaxtic_bar(top15, color_by = "Family", label_by = "Family", facet_by = NULL, grid_by = NULL)`

This didn't change the X axis order. Any suggestions? Thank you

Making a stacked bar chart with different taxonomic levels

Hello,
When I run the top 20 taxa on my data, my results come back with different taxonomic levels (mostly genus but some are only down to family, etc.). How can I run the bar chart so that all of the top 20 taxa are assigned the lowest taxonomic level possible? When I run the code using just "Genus," I get a lot of "unknown."

top20 <- get_top_taxa(ps_norm, 20, relative = TRUE, discard_other = FALSE, other_label = "Other")
top20g <- fantaxtic_bar(top20, color_by = "Genus", label_by = "Genus", facet_by = NULL, grid_by = NULL, other_color = "Grey") -> ptop15

Also...is there a way to combine ASVs of the same genus in the graph? For instance, I would like to combine all the ASVs that are labelled Flavobacteria.

Finally...is there a way to figure out what % of the ASVs are classified as "unknown?"

Thank you!

Sorting stacked barplot based on abundance values

Hey,

I am using fantaxtic to create a stacked barplot of relative abundances for each of my samples; however, I am having difficulties controlling the order of each block within each sample. I would like to have these blocks sorted by their abundances in descending or ascending order for easier viewing.

I am using the following code:

top_nested <- nested_top_taxa(ps.rel,
                              top_tax_level = "Phylum",
                              nested_tax_level = "Genus",
                              n_top_taxa = 15, 
                              n_nested_taxa = 15)

plot_nested_bar(ps_obj = top_nested$ps_obj,
                top_level = "Phylum",
                nested_level = "Genus")

Is there a way to change this default behavior?

Relative abundance option in get_top_taxa does not turn off

Hi,

Great package!!
I checked the code, and it seems to be related with the fantaxtic_barplot. It turns the table into relative abundance regardless of what the get_top_taxa does. Is it anything I can do? Did I do something wrong?

Thanks!

Unable to install Fantaxtic

I’m trying to install Fantaxtic but I’m unable to do so using remotes or devtools as below. Is there any way you can assist?

remotes::install_github("gmteunisse/Fantaxtic")
Skipping install of 'fantaxtic' from a github remote, the SHA1 (7b422c5) has not changed since last install.
Use force = TRUE to force installation
devtools::install_github("gmteunisse/Fantaxtic")
Skipping install of 'fantaxtic' from a github remote, the SHA1 (7b422c5) has not changed since last install.
Use force = TRUE to force installation

How to make taxa colors congruent along plots?

Hello developer, I'm working a set of more than 200 samples separated in different study conditions. From my 3 experimental conditions (with different number of samples )I elaborated three phyloseq objects and made plots of them . The problem is that a same ASV in one plot is showed in different color on the other plots. Is there a way in which I can maintain the same color for a respective taxon along the three plots?

This is what I did:

get top 15 ASVs

top_asvs_nasal_RN = get_top_taxa(physeq_obj = refiltered_nasal_RN, n = 15 , relative = TRUE,
discard_other = FALSE, other_label = "Other")

top_asvs_nasal_AR = get_top_taxa(physeq_obj = refiltered_nasal_AR, n = 15, relative = TRUE,
discard_other = FALSE, other_label = "Other")

top_asvs_nasal_NO = get_top_taxa(physeq_obj = refiltered_nasal_NO, n = 15, relative = TRUE,
discard_other = FALSE, other_label = "Other")

create labels for missing taxonomic ranks

top_asvs_nasal_RN = name_taxa(top_asvs_nasal_RN, label = "Unknown", species = T, other_label = "Other")
top_asvs_nasal_AR = name_taxa(top_asvs_nasal_AR, label = "Unknown", species = T, other_label = "Other")
top_asvs_nasal_NO = name_taxa(top_asvs_nasal_NO, label = "Unknown", species = T, other_label = "Other")

generate fantaxtic plots

fantaxtic_bar(top_asvs_nasal_RN, color_by = "Phylum", label_by = "Species", other_label = "Other")
fantaxtic_bar(top_asvs_nasal_AR, color_by = "Phylum", label_by = "Species", other_label = "Other")
fantaxtic_bar(top_asvs_nasal_NO, color_by = "Phylum", label_by = "Species", other_label = "Other")

the three above plots have different colors for the same taxon

label_duplicate_taxa does not generate unique labels

label_duplicate_taxa does not generate unique labels when asv_as_id = F. The reason seems to be that taxa with different upstream taxonomy can have the same downstream tax label, e.g. Clostridia. This can largely be resolved by setting asv_as_id = T, but requires a proper solution.

require("phyloseq")
require("fantaxtic")
require("tidyverse")

# Load data
data(GlobalPatterns)

# Generate (unique) species names using fantaxtic
ps <- name_na_taxa(GlobalPatterns)
ps <- label_duplicate_taxa(ps, asv_as_id = F)

# Count non-unique species
tax_table(ps) %>%
  data.frame(taxid = row.names(.)) %>%
  group_by(Species) %>%
  summarise(n = n()) %>%
  filter(n > 1L)

How to install fantaxtic package?

Hello,
I am trying to install the package from both devtools and remotes however I end up all the time with these errors.

if(!"devtools" %in% installed.packages()){ + install.packages("devtools") + }

devtools::install_github("gmteunisse/fantaxtic")
Downloading GitHub repo gmteunisse/fantaxtic@HEAD Error in utils::download.file(url, path, method = method, quiet = quiet, : cannot open URL 'https://api.github.com/repos/gmteunisse/fantaxtic/tarball/HEAD'
devtools::install_github("gmteunisse/ggnested")
Downloading GitHub repo gmteunisse/ggnested@HEAD Error in utils::download.file(url, path, method = method, quiet = quiet, : cannot open URL 'https://api.github.com/repos/gmteunisse/ggnested/tarball/HEAD'
--

if (!require("remotes")) { + install.packages("remotes") + }
remotes::install_github("gmteunisse/fantaxtic")
Downloading GitHub repo gmteunisse/fantaxtic@HEAD Error in utils::download.file(url, path, method = method, quiet = quiet, : cannot open URL 'https://api.github.com/repos/gmteunisse/fantaxtic/tarball/HEAD'
require("fantaxtic")
Loading required package: fantaxtic Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called ‘fantaxtic’
--

It would be a great help if you could guide me for finding problem and tentative solution for it! |

Fail to install Fantaxtic

Hello! Fantaxtic seems to have some brilliant functions and I am keen to try it on my dataset.
Unfortunately, I cannot find the way to complete it's installation. I have tried both through remotes and devtools but nothing has worked. I've updated packages that needed it and re-installed rlang.
This is the error message I get:

> devtools::install_github("gmteunisse/Fantaxtic")
Downloading GitHub repo gmteunisse/Fantaxtic@HEAD
Skipping 1 packages not available: phyloseq
√  checking for file 'C:\Users\User\AppData\Local\Temp\RtmpoN2hMC\remotes31401b77e5\gmteunisse-Fantaxtic-4653973/DESCRIPTION' ...
-  preparing 'fantaxtic':
√  checking DESCRIPTION meta-information ... 
-  checking for LF line-endings in source and make files and shell scripts
-  checking for empty or unneeded directories
-  building 'fantaxtic_0.1.0.tar.gz'
   
Installing package into ‘C:/Users/User/Documents/R/win-library/4.0’
(as ‘lib’ is unspecified)
* installing *source* package 'fantaxtic' ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
Error: (converted from warning) package 'ggplot2' was built under R version 4.0.3
Execution halted
ERROR: lazy loading failed for package 'fantaxtic'
* removing 'C:/Users/User/Documents/R/win-library/4.0/fantaxtic'
Error: Failed to install 'fantaxtic' from GitHub:
  (converted from warning) installation of package ‘C:/Users/User/AppData/Local/Temp/RtmpoN2hMC/file31405bba16c6/fantaxtic_0.1.0.tar.gz’ had non-zero exit status

I use a Windows machine and my R version is 4.0.1 .
Any suggestions?
V.

Top taxa in each individual sample

Hi, I need to see what are the top taxa in each individual sample. Is there a way to do that using fantaxtic? I can only see the top taxa across all samples in the entire phyloseq object.

plot_nested_bar: Specify colors for each bacterial order

Hello, I need to specify colors for each of my bacterial orders so I can compare across plots. I tried using this code but the plot comes up with randomly chosen colors by the program. Could you help me troubleshoot? Thank you!

ps <- GlobalPatterns
ps_norm <- transform_sample_counts(ps, function(x) x / sum(x) )
order <- tax_glom(ps_norm, taxrank = "Order")
top_order <- top_taxa(order, n_taxa = 15)
plot_nested_bar(ps_obj = top_order$ps_obj, top_level = "Phylum",
nested_level = "Family",
palette=c(Frankiales = "paleturquoise1",
Micrococcales = "turquoise2",
Microtrichales = "turquoise3",
Bacteroidales = "gold",
Cytophagales = "goldenrod2",
Flavobacteriales = "goldenrod3",
Burkholderiales = "mistyrose",
Enterobacterales = "pink",
Pseudomonadales = "palevioletred1",
Rhizobiales = "palevioletred3",
Sphingmonadales = "maroon1",
Rhodobacterales = "violetred4",
Xanthomonadales = "palevioletred4",
Verrucomicrobiota = "black"))

Change legend text size

Hi!

I am trying to change the size of the legend text in a nested plot, but when I add theme(legend.text= element_text(size=8)), I get the error:

Error in `ggplot_add()`:
! Problem merging the `legend.text` theme element
Caused by error in `merge_element()`:
! Only elements of the same class can be merged

I'm wondering if there is a way to change the font size and other parameters of the legend?

Cheers!

a

Dear

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